Impact of Joule Heating and pH on Biosolids Electro-Dewatering
Bibliographic record
Abstract
Electro-dewatering (ED) is a novel technology to reduce the overall costs of residual biosolids processing, transport, and disposal. In this study, we investigated Joule heating and pH as parameters controlling the dewaterability limit, dewatering rate, and energy efficiency. Temperature-controlled electrodes revealed that Joule heating enhances water removal by increasing evaporation and electro-osmotic flow. High temperatures increased the dewatering rate, but had little impact on the dewaterability limit and energy efficiency. Analysis of horizontal layers after 15-min ED suggests electro-osmotic flow reversal, as evidenced by a shifting of the point of minimum moisture content from the anode toward the cathode. This flow reversal was also confirmed by the pH at the anode being below the isoelectric point, as ascertained by pH titration. The important role of pH on ED was further studied by adding acid/base solutions to biosolids prior to ED. An acidic pH reduced the biosolids charge while simultaneously increasing the dewatering efficiency. Thus, process optimization depends on trade-offs between speed and efficiency, according to physicochemical properties of the biosolids microstructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".